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We have a data containing data of 1320 types of Cryptocurrencies. This data contains about 65000 observations of 10 variables. These 10 variables are Date, Open, High, Low, Close, Volume, Market.Cap, coin and Delta.

Date : Date of observation

Open : Opening price on the given day

High : Highest price on the given day

Low : Lowest price on the given day

Close : Closing price on the given day

coin : Name of the cryptocurrency

Volume : Volume of transactions on the given day

Market Cap : Market capitalization in USD

So to find out the most dominant Cryptocurrencies in the market, we plotted a bar graph of the Top 5 Cryptocurrencies in the market.

  setwd("C:/Users/Saurabh/Desktop/8th Sem Project/Data")
  file.list <- list.files(path="C:/Users/Saurabh/Desktop/8th Sem Project/Data")
  coinName = c()
  highestMarketCap = c()
  for (i in 1:length(file.list)){
    file.df <- read.csv(file.list[i],header = TRUE)
    maxVal <- gsub(",","",max(as.character(file.df$Market.Cap)))
    maxVal <- as.numeric(maxVal)
    highestMarketCap <- c(highestMarketCap,maxVal)
    coinName <- c(coinName, as.character(file.df$coin[[1]]))
  }

  newdf <- data.frame(coinName,highestMarketCap)
  library(dplyr)

  newdf <- arrange(newdf,coinName,highestMarketCap)

  newdf <- arrange(newdf,desc(highestMarketCap))
  topfive <- newdf[seq(1:5),]
  library(ggplot2)

  ggplot(topfive, aes(x=topfive$coinName,y = topfive$highestMarketCap,fill=topfive$coinName))     +geom_bar(stat = "identity")+xlab("Coins")+ylab("Market Cap")+ggtitle("Top 5 Cryptocurrencies")

As we have seen above, the top 4 Cryptocurrencies by their Market Cap are Bitcoin(btc), Bitcoin Cash(bch), Etherium(ETH), DOGEE and KIN.

So here we plot a graph showing the highest price attained by each of these Cryptocurrencies over a span of some years.

  setwd("C:/Users/Saurabh/Desktop/8th Sem Project/Data")
  btcData <- read.csv('btc.csv')
  btcData$Date<-as.character(btcData$Date)
  btcData$Date <- as.Date(btcData$Date,format='%Y-%m-%d')
  ethData <- read.csv('ETH.csv')
  ethData$Date<-as.character(ethData$Date)
  ethData$Date <- as.Date(ethData$Date,format='%Y-%m-%d')
  bchData <- read.csv('BCH.csv')
  bchData$Date<-as.character(bchData$Date)
  bchData$Date <- as.Date(bchData$Date,format='%Y-%m-%d')
  DOGEData <- read.csv('DOGE.csv')
  DOGEData$Date<-as.character(DOGEData$Date)
  DOGEData$Date <- as.Date(DOGEData$Date,format='%Y-%m-%d')
  KinData <- read.csv('KIN.csv')
  KinData$Date<-as.character(KinData$Date)
  KinData$Date <- as.Date(KinData$Date,format='%Y-%m-%d')
  library(dplyr)
  df1<- btcData %>%bind_rows(ethData,bchData,DOGEData,KinData)

  plot_ly(x=df1$Date,y=df1$High,type='scatter',mode='lines',color=df1$coin)%>%
  layout(xaxis =list(title='Year'), yaxis = list(title='Price in USD'))

As it is evident from the above Time Series, that Bitcoin is the Cryptocurrency with the highest valuation throughout the the time. Bitcoin Cash remains second. The other two coins, Ether and Litecoin always had their prcies lower than BTC and BCH but they too saw a huge increase in the market cap.

setwd("C:/Users/Saurabh/Desktop/8th Sem Project/Data")
btcData<- read.csv('btc.csv')
bchData <- read.csv('bch.csv')
ethData <- read.csv('ETH.csv')
DogeData <- read.csv('Doge.csv')
KinData <- read.csv('KIN.csv')

btcData$Date<-as.character(btcData$Date)
btcData$Date <- as.Date(btcData$Date,format='%Y-%m-%d')


ethData$Date<-as.character(ethData$Date)
ethData$Date <- as.Date(ethData$Date,format='%Y-%m-%d')


bchData$Date<-as.character(bchData$Date)
bchData$Date <- as.Date(bchData$Date,format='%Y-%m-%d')

DogeData$Date<-as.character(DogeData$Date)
DogeData$Date <- as.Date(DogeData$Date,format='%Y-%m-%d')

KinData$Date<-as.character(KinData$Date)
KinData$Date <- as.Date(KinData$Date,format='%Y-%m-%d')

openData <-  function(coinData){
  print(ggplot(data=coinData,aes(x=coinData$Date,y=coinData$Close))+
          geom_line(color="blue"))
}

closeData <-  function(coinData){
  print(ggplot(data=coinData,aes(x=coinData$Date,y=coinData$Close))+
          geom_line(color="yellow"))
}
highData <-  function(coinData){
  print(ggplot(data=coinData,aes(x=coinData$Date,y=coinData$High))+
          geom_line(color="blue"))
}

lowData <-  function(coinData){
  print(ggplot(data=coinData,aes(x=coinData$Date,y=coinData$Low))+
          geom_line(color="yellow"))
}

openData(btcData)

closeData(btcData)

lowData(btcData)

highData(btcData)